When I was in school, I always found that the blackboard was the best teaching tool (as opposed to say a pre-prepared static slide in PowerPoint). As a student, I found it really helpful to see a concept get built up from nothing, and accordingly when I was a teaching assistant in economics, I also preferred using the blackboard to build up concepts together with my students (I also found it more fun as it was now a collaborative experience). Now in the business world, developing concepts is still as important as when I was in school, but using a blackboard will likely get me… looks… Luckily, there’s a very neat alternative: the animation!

Dataset

This post will be more on creating animations, rather than focus on the specific dataset being animated. Having said that, an interesting dataset will make this all the more exciting.

Gapminder has put together a series of data about the world across time and has made the video that has been described as ‘The best stats you’ve ever seen’. I certainly agree.

Lately in Canada, there has been some concern over the number of babies the country is producing. We shall then take a look at how income per person has been affecting the number of children women have given birth to throughout the years across all regions around the world.

Pulling in the data

We’ll begin with downloading the data.

I’m going to do some data cleanup but I will skip the code to keep this post more brief.

[…data cleanup…]

After the data cleanup, we will join all the datasets together for one unified set:

# Join datasets
data.join <- left_join(data.income.clean, data.babies.clean) %>%
        left_join(data.pop.clean) %>%
        left_join(data.region.clean)

data.join[complete.cases(data.join),] -> data.join

Here’s a random sample of what 10 data points look like:

country	year	income	babies	pop	region
Sao Tome and Principe	1956	1194	6.19	59744	Sub-Saharan Africa
Cambodia	2008	2442	3.05	13933660	East Asia & Pacific
New Zealand	1962	16646	3.51	2477328	East Asia & Pacific
Serbia	1968	8948	2.42	7998000	Europe & Central Asia
Honduras	2002	3563	3.78	6499001	Latin America & Caribbean
Guatemala	2013	7063	3.78	15690793	Latin America & Caribbean
Sao Tome and Principe	1975	2426	6.54	82607	Sub-Saharan Africa
Philippines	1955	2376	7.37	22179103	East Asia & Pacific
Israel	2002	24650	2.91	6240215	Middle East & North Africa
Kyrgyz Republic	1972	3510	4.72	3103694	Europe & Central Asia

Method 1: `gganimate` with `tweenr`

First up, is a combo-solution that directly extends the ggplot2 universe: the package gganimate developed by David Robinson (@drob) along with the package tweenr developed by Thomas Lin Pedersen (@thomasp85).

Let’s develop this animation.

library(gganimate)
library(tweenr)

# Tween for smoother animations
data.join.tween <- data.join %>%
        rename(x = income,
               y = babies,
               time = year,
               id = country) %>%
        mutate(ease = "linear") %>%
        select(-region) %>%
        tween_elements("time", "id", "ease", nframes = 1000)

# Re-add prior data
data.join.tween <- inner_join(data.join.tween, data.region.clean, by = c(".group" = "country")) 

# Plot
p <- ggplot(data.join.tween, aes(x = x, y = y)) +
        geom_point(aes(size = pop, frame = .frame, colour = region), alpha = 0.7) +  
        xlab("GDP per capita") +
        ylab("Number of Babies Born per Woman") +
        theme_minimal(base_size = 16) +
        geom_smooth(aes(group = .frame, frame = .frame), method = "loess", 
                    color = "black", linetype = "dashed", se = F, size = 0.5) +
        theme(legend.position="none") +
        scale_x_log10(labels = dollar) + 
        scale_size_area(guide = FALSE, max_size = 20) +
        scale_color_brewer(name = "", palette = "Set2") +
        facet_wrap(~region)


# Animate Plot
gganimate(p, title_frame = T, interval = 0.02, "../../static/img/gganimate.gif", 
          ani.width = 800, ani.height = 800, 
          ani.res = 90) #<- Not run to save render time

Voila, a pretty nice looking animated graph if I may say so myself 😃.

Method 2: `plotly`

The second method is via the plotly package, developed by a Canadian company by the same name.

plotly also has a function which allows you to translate a ggplot2 graph into a plotly graph which we will use below.

# Generate base ggplot2 graph
p2 <- ggplot(data.join, aes(x = income, y = babies)) +
        geom_point(aes(size = pop, frame = year, colour = region), alpha = 0.7) +  
        xlab("GDP per capita") +
        ylab("Number of Babies Born per Woman") +
        theme_minimal(base_size = 10) +
        geom_smooth(aes(group = year, frame = year), method = "loess", 
                    color = "black", linetype = "dashed", se = F, size = 0.5) +
        theme(legend.position="none") +
        scale_x_log10(labels = dollar) + 
        scale_size_area(guide = FALSE, max_size = 20) +
        scale_color_brewer(name = "", palette = "Set2") +
        facet_wrap(~region)

# Create plotly graph
ggplotly(p2, height = 800, width = 800) %>%
        animation_opts(frame = 300,
                       easing = "linear",
                       redraw = FALSE)

Dataset

Pulling in the data

Method 1: gganimate with tweenr

Method 2: plotly

Method 1: `gganimate` with `tweenr`

Method 2: `plotly`